Verbyla, K. | bioRxiv Science

bioRxiv2022

Detecting epistatic interactions in genomic data using Random Forests

Epistatic interactions can play an important role in the genetic mechanisms that control phenotypic variation. However, identifying these interactions in high dimensional genomic data can be very challenging due to the large computational burden induced by the high volume of combinatorial tests that have to be performed to explore the entire search space. Random Forests Decision Trees are widely used in a variety of disciplines and are often said to detect interactions. However, Random Forests models do not explicitly detect variable interactions. Most Random Forests based methods that claim to detect interactions rely on different forms of variable importance measures that suffer when the interacting variables have very small or no marginal effects. The proposed Random Forests based method detects interactions using a two-stage approach and is computationally efficient. The approach is demonstrated and validated through its application on several simulated datasets representing different data structures with respect to genomic data and trait heritabilities. The method is also applied to two high dimensional genomics data sets to validate the approach. In both cases, the method results were used to identify several genes closely positioned to the interacting markers that showed strong biological potential for contributing to the genetic control for the respective traits tested. Contacthawlader.almamun@csiro.au

bioinformatics↗

bioRxiv2019

The complex genetic architecture of recombination and structural variation in wheat uncovered using a large 8-founder MAGIC population

BackgroundIdentifying the genetic architecture of complex traits requires access to populations with sufficient genetic diversity and recombination. Multi-parent Advanced Generation InterCross (MAGIC) populations are a powerful resource due to their balanced population structure, allelic diversity and enhanced recombination. However, implementing a MAGIC population in complex polyploids such as wheat is not trivial, as wheat harbours many introgressions, inversions and other genetic factors that interfere with linkage mapping.\n\nResultsBy utilising a comprehensive crossing strategy, additional rounds of mixing and novel genotype calling approaches, we developed a bread wheat eight parent MAGIC population made up of more than 3000 fully genotyped recombinant inbred lines derived from 2151 distinct crosses, and achieved a dense genetic map covering the complete genome. Further rounds of inter-crossing led to increased recombination in inbred lines, as expected. The comprehensive and novel approaches taken in the development and analysis of this population provide a platform for genetic discovery in bread wheat. We identify previously unreported structural variation highlighted by segregation distortion, along with the identification of epistatic allelic interactions between specific founders. We demonstrate the ability to conduct high resolution QTL mapping using the number of recombination events as a trait, and identify several significant QTLs explaining greater than 50% of the variance.\n\nConclusionsWe report on a novel and effective resource for genomic and trait exploration in hexaploid wheat, that can be used to detect small genetic effects and epistatic interactions due to the high level of recombination and large number of lines. The interactions and genetic effects identified provide a basis for ongoing research to understand the basis of allelic frequencies across the genome, particularly where economically important loci are involved.

plant biology↗